SNA Descritive Analysis from “Projeto Redes de Atenção às pessoas que consomem álcool e outras Drogas em Juiz de Fora-MG Brazil” - SNArRDJF
Here you can find a basic script to analysis data from SNArRDJF - this script was elaborated considering its use for orther matrix adjacency data from SNArRDJF - Here we are going to analyse:
########################## Basic Preparation ##### `#########################
rm(list = ls()) # removing previous objects to be sure that we don't have objects conflicts name
load("~/SNArRDJF/Robject/var1_data.RData")
suppressMessages(library(RColorBrewer))
#suppressMessages(library(car))
#suppressMessages(library(xtable))
suppressMessages(library(igraph))
#suppressMessages(library(miniCRAN))
#suppressMessages(library(magrittr))
#suppressMessages(library(keyplayer))
#suppressMessages(library(dplyr))
#suppressMessages(library(feather))
#suppressMessages(library(visNetwork))
#suppressMessages(library(knitr))
suppressMessages(library(DT))
#In order to get dinamic javascript object install those ones. If you get problems installing go to Stackoverflow.com and type your error to discover what to do. In some cases the libraries need to be intalled in outside R libs.
#devtools::install_github("wch/webshot")
#webshot::install_phantomjs()
set.seed(123)
#var1<-simplify(var1) #Simplify
• For undirected graphs:
– Actor centrality - involvement (connections) with other actors
• For directed graphs:
– Actor centrality - source of the ties (outgoing edges)
– Actor prestige - recipient of many ties (incoming edges)
In general - high centrality degree means direct contact with many other actors
V(var1)$indegree<-degree(var1, mode = "in") # Actor prestige - recipient of many ties (incoming edges)
V(var1)$outdegree <- degree(var1, mode = "out") # Actor centrality - source of the ties (outgoing edges)
V(var1)$totaldegree <- degree(var1, mode = "total")
var1_indegree<-degree(var1, mode = "in")
var1_outdegree<-degree(var1, mode = "out")
var1_totaldegree<-degree(var1, mode = "total")
##in
summary(var1_indegree)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 4.000 6.193 6.500 94.000
sd(var1_indegree)
## [1] 9.144324
hist(degree(var1, mode = "in", normalized = F), ylab="Frequency", xlab="Degree", breaks=vcount(var1)/10, main="Histogram of Indegree Nodes - 3_REFERENCIA DE ENVIO (var1)")
##out
summary(var1_outdegree)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.500 3.000 6.193 6.500 93.000
sd(var1_outdegree)
## [1] 11.47486
hist(degree(var1, mode = "out", normalized = F), ylab="Frequency", xlab="Degree", breaks=vcount(var1)/10, main="Histogram of Outdegree Nodes - 3_REFERENCIA DE ENVIO (var1)")
##all
summary(var1_totaldegree)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 3.00 7.00 12.39 12.50 187.00
sd(var1_totaldegree)
## [1] 19.25191
hist(degree(var1, mode = "all", normalized = F), ylab="Frequency", xlab="Degree", breaks=vcount(var1)/10, main="Histogram of All Degree Nodes - 3_REFERENCIA DE ENVIO (var1)")
A slightly more nuanced metric is “strength centrality”, which is defined as the sum of the weights of all the connections for a given node. This is also sometimes called “weighted degree centrality”
V(var1)$var1_strength<- strength(var1, weights=E(var1)$weight)
var1_strength<- strength(var1, weights=E(var1)$weight)
summary(var1_strength)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 3.00 7.00 12.39 12.50 187.00
sd(var1_strength)
## [1] 19.25191
hist(strength(var1, weights=E(var1)$weight), ylab="Frequency", xlab="Degree", breaks=vcount(var1)/10, main="Histogram of Strength Degree Nodes - 3_REFERENCIA DE ENVIO (var1)")
V(var1)$indegree_n<-degree(var1, mode = "in", normalized = T)
V(var1)$outdegree_n<- degree(var1, mode = "out", normalized = T)
V(var1)$totaldegree_n<- degree(var1, mode = "total", normalized = T)
var1_indegree_n<-degree(var1, mode = "in", normalized = T)
var1_outdegree_n<-degree(var1, mode = "out", normalized = T)
var1_totaldegree_n<-degree(var1, mode = "total", normalized = T)
summary(var1_indegree_n)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.01075 0.02151 0.03329 0.03495 0.50540
sd(var1_indegree_n)
## [1] 0.04916303
hist(degree(var1, mode = "in", normalized = T), ylab="Frequency", xlab="Normalized Degree", breaks=vcount(var1)/10, main="Histogram of Normalized Indegree Nodes - 3_REFERENCIA DE ENVIO (var1)")
summary(var1_outdegree_n)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.002688 0.016130 0.033290 0.034950 0.500000
sd(var1_outdegree_n)
## [1] 0.0616928
hist(degree(var1, mode = "out", normalized = T), ylab="Frequency", xlab="Normalized Degree", breaks=vcount(var1)/10, main="Histogram of Normalized Outdegree Nodes - 3_REFERENCIA DE ENVIO (var1)")
summary(var1_totaldegree_n)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.01613 0.03763 0.06659 0.06720 1.00500
sd(var1_totaldegree_n)
## [1] 0.1035049
hist(degree(var1, mode = "all", normalized = T), ylab="Frequency", xlab="Normalized Degree", breaks=vcount(var1)/10, main="Histogram of Normalized All Degree Nodes - 3_REFERENCIA DE ENVIO (var1)")
V(var1)$var1_centr_degree <- centralization.degree(var1)$res
var1_centr_degree <- centralization.degree(var1)
var1_centr_degree$centralization
## [1] 0.4719187
var1_centr_degree$theoretical_max
## [1] 69192
var1_degree.distribution<-degree.distribution(var1)
summary(var1_degree.distribution)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.000000 0.000000 0.005319 0.000000 0.133700
sd(var1_degree.distribution)
## [1] 0.01795872
hist(degree.distribution(var1), breaks=vcount(var1)/10, ylab="Frequency", xlab="Degree Distribuition", main="Histogram of Degree Distribuition - 3_REFERENCIA DE ENVIO (var1)")
dd <- degree.distribution(var1, cumulative=T, mode="all")
plot(dd, pch=19, cex=1, col="orange", xlab="Degree", ylab="Cumulative Frequency", main= "Cumulative Frequency of 3_REFERENCIA DE ENVIO (var1) ")
dd.var1 <- degree.distribution(var1)
d <- 1:max(degree(var1))-1
ind <- (dd.var1 != 0)
plot(d[ind],
dd.var1[ind],
log="xy",
col="blue",
xlab=c("Log-Degree"),
ylab=c("Log-Intensity"),
main="Log-Log Degree Distribution For 3_REFERENCIA DE ENVIO (var1)"
)
The neighborhood of a given order y of a vertex v includes all vertices which are closer to v than the order. Ie. order y=0 is always v itself, order 1 is v plus its immediate neighbors, order 2 is order 1 plus the immediate neighbors of the vertices in order 1, etc.
var1_simplified<-simplify(var1)
var1_a.nn.deg <- graph.knn(var1_simplified, weights =E(var1_simplified)$weight)$knn %>% round(1)
V(var1_simplified)$var1_a.nn.deg <- graph.knn(var1_simplified, weights=E(var1_simplified)$weight)$knn
d<-cbind(V(var1_simplified)$LABEL_COR,var1_a.nn.deg)
datatable(d)
plot(degree(var1_simplified),
var1_a.nn.deg,
log="xy",
col="goldenrod",
xlab=c("Log Vertex Degree"),
ylab=c("Log Average Neighbor Degree"),
main="Average Neighbor Degree vs Vertex Degree - Log-Log Scale for 3_REFERENCIA DE ENVIO (var1)"
)
var1_a.nn.deg_w <- graph.knn(var1_simplified, weights=E(var1_simplified)$weight)$knn %>% round(1)
V(var1_simplified)$var1_a.nn.deg_w <-var1_a.nn.deg <- graph.knn(var1_simplified, weights=E(var1_simplified)$weight)$knn
summary(var1_a.nn.deg_w)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.50 27.00 40.60 52.77 79.12 161.00 1
sd(var1_a.nn.deg_w, na.rm = T)
## [1] 33.90495
d<-cbind(V(var1_simplified)$LABEL_COR,var1_a.nn.deg_w)
datatable(d)
plot(degree(var1_simplified),
var1_a.nn.deg,
log="xy",
col="goldenrod",
xlab=c("Log Vertex Degree"),
ylab=c("Log Average Neighbor Degree"),
main="Average Weighted Neighbor Degree vs Vertex Degree - Log-Log Scale For Weighted 3_REFERENCIA DE ENVIO (var1)"
)
var1_indegree<-degree(var1, mode = "in")
var1_outdegree<-degree(var1, mode = "out")
var1_totaldegree<-degree(var1, mode = "total")
var1_strength<- strength(var1, weights=E(var1)$weight)
var1_indegree_n<-degree(var1, mode = "in", normalized = T) %>% round(3)
var1_outdegree_n<-degree(var1, mode = "out", normalized = T) %>% round(3)
var1_totaldegree_n<-degree(var1, mode = "total", normalized = T) %>% round(3)
var1_centr_degree <- centralization.degree(var1)$res
var1_a.nn.deg <- graph.knn(var1_simplified)$knn %>% round(1)
var1_a.nn.deg_w <- graph.knn(var1_simplified, weights=E(var1_simplified)$weight)$knn %>% round(1)
var1_df_degree <- data.frame(var1_indegree,
var1_outdegree,
var1_totaldegree,
var1_indegree_n,
var1_outdegree_n,
var1_totaldegree_n,
var1_strength,
var1_centr_degree,
var1_a.nn.deg,
var1_a.nn.deg_w) %>% round(3)
#Adding type
var1_df_degree <-cbind(var1_df_degree, V(var1)$LABEL_COR)
#Adding names
names(var1_df_degree) <- c("In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree","Type")
#Ordering Variables
var1_df_degree<-var1_df_degree[c("Type","In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree")]
datatable(var1_df_degree, filter = 'top')
aggdata_mean <-aggregate(var1_df_degree, by=list(var1_df_degree$Type), FUN=mean, na.rm=TRUE)
#Removing Type variable
aggdata_mean<-aggdata_mean[,-c(2)]
names(aggdata_mean) <- c("Group", "In Degree(M)", "Out Degree(M)", "Total Degree(M)","In Degree Normalized(M)", "Out Degree Normalized(M)", "Total Degree Normalized(M)", "Strength(M)","Centralization Degree(M)","Average Neighbor Degree(M)","Average Weighted Neighbor Degree(M)")
aggdata_sd <-aggregate(var1_df_degree, by=list(var1_df_degree$Type), FUN=sd, na.rm=TRUE)
#Removing Type variable
aggdata_sd<-aggdata_sd[,-c(2)]
names(aggdata_sd) <- c("Group", "In Degree(SD)", "Out Degree(SD)", "Total Degree(SD)","In Degree Normalized(SD)", "Out Degree Normalized(SD)", "Total Degree Normalized(SD)", "Strength(SD)","Centralization Degree(SD)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(SD)")
total_table <- merge(aggdata_mean,aggdata_sd,by="Group")
#Rounding
Group<-total_table[,c(1)] #Keeping group
total_table<-total_table[,-c(1)] %>% round(2) #Rouding
total_table<-cbind(Group,total_table) #Binding toghter
#Organizing Variabels
total_table<-total_table[c("Group","In Degree(M)","In Degree(SD)", "Out Degree(M)", "Out Degree(SD)","Total Degree(M)", "Total Degree(SD)", "In Degree Normalized(M)", "In Degree Normalized(SD)", "Out Degree Normalized(M)", "Out Degree Normalized(SD)", "Total Degree Normalized(M)", "Total Degree Normalized(SD)", "Strength(M)","Strength(SD)", "Centralization Degree(M)","Centralization Degree(SD)","Average Neighbor Degree(M)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(M)", "Average Weighted Neighbor Degree(SD)")]
datatable(total_table, filter = 'top')
var1_df_degree <- data.frame(var1_indegree,
var1_outdegree,
var1_totaldegree,
var1_indegree_n,
var1_outdegree_n,
var1_totaldegree_n,
var1_strength,
var1_centr_degree,
var1_a.nn.deg,
var1_a.nn.deg_w) %>% round(3)
#Adding type
var1_df_degree <-cbind(var1_df_degree, V(var1)$TIPO1)
#Adding names
names(var1_df_degree) <- c("In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree","Type")
#Ordering Variables
var1_df_degree<-var1_df_degree[c("Type","In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree")]
datatable(var1_df_degree, filter = 'top')
aggdata_mean <-aggregate(var1_df_degree, by=list(var1_df_degree$Type), FUN=mean, na.rm=TRUE)
#Removing Type variable
aggdata_mean<-aggdata_mean[,-c(2)]
names(aggdata_mean) <- c("Group", "In Degree(M)", "Out Degree(M)", "Total Degree(M)","In Degree Normalized(M)", "Out Degree Normalized(M)", "Total Degree Normalized(M)", "Strength(M)","Centralization Degree(M)","Average Neighbor Degree(M)","Average Weighted Neighbor Degree(M)")
aggdata_sd <-aggregate(var1_df_degree, by=list(var1_df_degree$Type), FUN=sd, na.rm=TRUE)
#Removing Type variable
aggdata_sd<-aggdata_sd[,-c(2)]
names(aggdata_sd) <- c("Group", "In Degree(SD)", "Out Degree(SD)", "Total Degree(SD)","In Degree Normalized(SD)", "Out Degree Normalized(SD)", "Total Degree Normalized(SD)", "Strength(SD)","Centralization Degree(SD)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(SD)")
total_table <- merge(aggdata_mean,aggdata_sd,by="Group")
#Rounding
Group<-total_table[,c(1)] #Keeping group
total_table<-total_table[,-c(1)] %>% round(2) #Rouding
total_table<-cbind(Group,total_table) #Binding toghter
#Organizing Variabels
total_table<-total_table[c("Group","In Degree(M)","In Degree(SD)", "Out Degree(M)", "Out Degree(SD)","Total Degree(M)", "Total Degree(SD)", "In Degree Normalized(M)", "In Degree Normalized(SD)", "Out Degree Normalized(M)", "Out Degree Normalized(SD)", "Total Degree Normalized(M)", "Total Degree Normalized(SD)", "Strength(M)","Strength(SD)", "Centralization Degree(M)","Centralization Degree(SD)","Average Neighbor Degree(M)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(M)", "Average Weighted Neighbor Degree(SD)")]
datatable(total_table, filter = 'top')
var1_df_degree <- data.frame(var1_indegree,
var1_outdegree,
var1_totaldegree,
var1_indegree_n,
var1_outdegree_n,
var1_totaldegree_n,
var1_strength,
var1_centr_degree,
var1_a.nn.deg,
var1_a.nn.deg_w) %>% round(3)
#Adding type
var1_df_degree <-cbind(var1_df_degree, V(var1)$TIPO2)
#Adding names
names(var1_df_degree) <- c("In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree","Type")
#Ordering Variables
var1_df_degree<-var1_df_degree[c("Type","In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree")]
datatable(var1_df_degree, filter = 'top')
aggdata_mean <-aggregate(var1_df_degree, by=list(var1_df_degree$Type), FUN=mean, na.rm=TRUE)
#Removing Type variable
aggdata_mean<-aggdata_mean[,-c(2)]
names(aggdata_mean) <- c("Group", "In Degree(M)", "Out Degree(M)", "Total Degree(M)","In Degree Normalized(M)", "Out Degree Normalized(M)", "Total Degree Normalized(M)", "Strength(M)","Centralization Degree(M)","Average Neighbor Degree(M)","Average Weighted Neighbor Degree(M)")
aggdata_sd <-aggregate(var1_df_degree, by=list(var1_df_degree$Type), FUN=sd, na.rm=TRUE)
#Removing Type variable
aggdata_sd<-aggdata_sd[,-c(2)]
names(aggdata_sd) <- c("Group", "In Degree(SD)", "Out Degree(SD)", "Total Degree(SD)","In Degree Normalized(SD)", "Out Degree Normalized(SD)", "Total Degree Normalized(SD)", "Strength(SD)","Centralization Degree(SD)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(SD)")
total_table <- merge(aggdata_mean,aggdata_sd,by="Group")
#Rounding
Group<-total_table[,c(1)] #Keeping group
total_table<-total_table[,-c(1)] %>% round(2) #Rouding
total_table<-cbind(Group,total_table) #Binding toghter
#Organizing Variabels
total_table<-total_table[c("Group","In Degree(M)","In Degree(SD)", "Out Degree(M)", "Out Degree(SD)","Total Degree(M)", "Total Degree(SD)", "In Degree Normalized(M)", "In Degree Normalized(SD)", "Out Degree Normalized(M)", "Out Degree Normalized(SD)", "Total Degree Normalized(M)", "Total Degree Normalized(SD)", "Strength(M)","Strength(SD)", "Centralization Degree(M)","Centralization Degree(SD)","Average Neighbor Degree(M)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(M)", "Average Weighted Neighbor Degree(SD)")]
datatable(total_table, filter = 'top')
#Set Seed
set.seed(123)
#Plotting based only on degree measures
edge.start <- ends(var1, es=E(var1), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(var1))
maxC <- rep(Inf, vcount(var1))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(var1, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(var1)$weight)
#PLotting
plot(var1,
layout=co,
edge.color=V(var1)$color[edge.start],
edge.arrow.size=(degree(var1)+1)/(30*mean(degree(var1))),
edge.width=E(var1)$weight/(10*mean(E(var1)$weight)),
edge.curved = TRUE,
vertex.size=log((degree(var1)+2))*(0.5*mean(degree(var1))),
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(var1,"LABEL_COR"),
vertex.label.cex=log(degree(var1)+2)/mean(degree(var1)),
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(var1)$LABEL_COR
b<-V(var1)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=2,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Vertex Degree Sized - 3_REFERENCIA DE ENVIO (var1)", sub = "Source: from authors ", cex = .5)
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median In Degree: %.2f\n Median Out Degree: %.2f",
median(degree(var1, mode="in")),
median(degree(var1, mode="out"))
))
#Set Seed
set.seed(123)
#Get Variable
V(var1)$var1_color_degree<-V(var1)$totaldegree %>% round(0)
#Creating brewer pallette
vertex_var1_color_degree<-
colorRampPalette(brewer.pal(length(unique(
V(var1)$var1_color_degree)), "RdBu"))(
length(unique(V(var1)$var1_color_degree)))
#Saving as Vertex properties
V(var1)$vertex_var1_color_degree<- vertex_var1_color_degree[as.numeric(cut(degree(var1),breaks =length(unique(V(var1)$var1_color_degree))))]
set.seed(123)
#Plotting based only on degree measures
edge.start <- ends(var1, es=E(var1), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(var1))
maxC <- rep(Inf, vcount(var1))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(var1, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(var1)$weight)
#PLotting
plot(var1,
layout=co,
#edge.color=V(var1)$color[edge.start],
edge.arrow.size=(degree(var1)+1)/1000,
edge.width=E(var1)$weight/10,
edge.curved = TRUE,
vertex.color=V(var1)$vertex_var1_color_degree,
vertex.size=log((degree(var1)+2))*10,
vertex.size=20,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(var1,"LABEL_COR"),
vertex.label.cex=log((degree(var1)+2))/10,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(var1)$var1_color_degree
b<-V(var1)$vertex_var1_color_degree
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
e<-e[order(e$a,decreasing=T),]
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=2,
bty="n",
ncol=1,
lty=1,
cex = .3)
#Adding Title
title("Network Vertex Degree Sized and Red to Blue - 3_REFERENCIA DE ENVIO (var1)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median In Degree: %.2f\nMedian Out Degree: %.2f",
median(degree(var1, mode="in")),
median(degree(var1, mode="out"))
))
#Set Seed
set.seed(123)
#Get Variable
V(var1)$var1_color_degree<-V(var1)$var1_centr_degree
#Creating brewer pallette
vertex_var1_color_degree<-
colorRampPalette(brewer.pal(length(unique(
V(var1)$var1_color_degree)), "Spectral"))(
length(unique(V(var1)$var1_color_degree)))
#Saving as Vertex properties
V(var1)$vertex_var1_color_degree<- vertex_var1_color_degree[as.numeric(cut(V(var1)$var1_color_degree,breaks =length(unique(V(var1)$var1_color_degree))))]
#Plotting based only on degree measures
edge.start <- ends(var1, es=E(var1), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(var1))
maxC <- rep(Inf, vcount(var1))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(var1, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(var1)$weight)
#PLotting
plot(var1,
layout=co,
edge.color=V(var1)$vertex_var1_color_degree[edge.start],
edge.arrow.size=(degree(var1)+1)/10000,
edge.width=E(var1)$weight/10,
edge.curved = TRUE,
vertex.color=V(var1)$vertex_var1_color_degree,
vertex.size=log((V(var1)$var1_centr_degree+2))*10,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(var1,"LABEL_COR"),
vertex.label.cex=log((degree(var1)+2))/10,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(var1)$var1_color_degree
b<-V(var1)$vertex_var1_color_degree
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
e<-e[order(e$a,decreasing=T),]
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=2,
bty="n",
ncol=1,
lty=1,
cex = .3)
#Adding Title
title("Network Vertex Centralization Degree Sized Spectral Colored - 3_REFERENCIA DE ENVIO (var1)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median In Degree: %.2f\nMedian Out Degree: %.2f",
median(degree(var1, mode="in")),
median(degree(var1, mode="out"))
))
#Set Seed
set.seed(123)
# Network elements with lower than meadian degree
higherthanmedian.network_var1<-V(var1)[degree(var1)<median(degree(var1))]
#Deleting vertices based in intersection betewenn var1
high_var1<-delete.vertices(var1, higherthanmedian.network_var1)
#Plotting based only on degree measures
edge.start <- ends(high_var1, es=E(high_var1), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(high_var1))
maxC <- rep(Inf, vcount(high_var1))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(high_var1, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(high_var1)$weight)
#PLotting
plot(high_var1,
layout=co,
edge.color=V(high_var1)$color[edge.start],
edge.arrow.size=(degree(high_var1)+1)/1000,
edge.width=E(high_var1)$weight/10,
edge.curved = TRUE,
vertex.size=log((V(high_var1)$var1_centr_degree+2))*10,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(high_var1,"LABEL_COR"),
vertex.label.cex=log((degree(high_var1)+2))/10,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(high_var1)$LABEL_COR
b<-V(high_var1)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=3,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Higher Than Median Degree - 3_REFERENCIA DE ENVIO (var1)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Mean In Degree: %.2f\n Mean Out Degree: %.2f",
mean(degree(high_var1, mode="in")),
mean(degree(high_var1, mode="out"))
)
)
#Set Seed
set.seed(123)
# Network elements with lower than meadian degree
lowerthanmedian.network_var1<-V(var1)[degree(var1)>median(degree(var1))]
#Deleting vertices based in intersection betewenn var1
small_var1<-delete.vertices(var1, lowerthanmedian.network_var1)
#Plotting based only on degree measures
edge.start <- ends(small_var1, es=E(small_var1), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(small_var1))
maxC <- rep(Inf, vcount(small_var1))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(small_var1, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(small_var1)$weight)
#PLotting
plot(small_var1,
layout=co,
edge.color=V(small_var1)$color[edge.start],
edge.arrow.size=(degree(small_var1)+1)/1000,
edge.width=E(small_var1)$weight/10,
edge.curved = TRUE,
vertex.size=log((V(small_var1)$var1_centr_degree+2))*20,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(small_var1,"LABEL_COR"),
vertex.label.cex=log((degree(small_var1)+2))/3,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(small_var1)$LABEL_COR
b<-V(small_var1)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=4,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Smaller Than Median Degree - 3_REFERENCIA DE ENVIO (var1)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Mean In Degree: %.2f\nMean Out Degree: %.2f",
mean(degree(small_var1, mode="in")),
mean(degree(small_var1, mode="out"))
)
)
#Set Seed
set.seed(123)
#Plotting based only on degree measures
edge.start <- ends(var1_simplified, es=E(var1_simplified), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(var1_simplified))
maxC <- rep(Inf, vcount(var1_simplified))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(var1_simplified, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(var1_simplified)$weight)
#Plotting based only on degree measures #var1_simplified_a.nn.deg
V(var1_simplified)$var1_a.nn.deg<-as.numeric(graph.knn(var1_simplified)$knn)
V(var1_simplified)$var1_a.nn.deg[V(var1_simplified)$var1_a.nn.deg=="NaN"]<-0
#PLotting
plot(var1_simplified,
layout=co,
edge.color=V(var1_simplified)$color[edge.start],
edge.arrow.size=sqrt((V(var1_simplified)$var1_a.nn.deg)^2+1)/1000,
edge.width=E(var1_simplified)$weight/100,
edge.curved = TRUE,
vertex.color=V(var1_simplified)$color,
vertex.size=(sqrt((V(var1_simplified)$var1_a.nn.deg)^2))/5,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(var1_simplified,"LABEL_COR"),
vertex.label.cex=(sqrt((V(var1_simplified)$var1_a.nn.deg)^2)+1)/500,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(var1_simplified)$LABEL_COR
b<-V(var1_simplified)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=4,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Average Neighbor Degree Sized - 3_REFERENCIA DE ENVIO (var1)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median Average Neighbor Degree: %.2f",
median((var1_a.nn.deg+1))
))
#Set Seed
set.seed(123)
#Plotting based only on degree measures
edge.start <- ends(var1_simplified, es=E(var1_simplified), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(var1_simplified))
maxC <- rep(Inf, vcount(var1_simplified))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(var1_simplified, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(var1_simplified)$weight)
#Plotting based only on degree measures #var1_a.nn.deg
V(var1_simplified)$var1_a.nn.deg_w<-as.numeric(graph.knn(var1_simplified, weights = E(var1_simplified)$weight)$knn)
V(var1_simplified)$var1_a.nn.deg_w[V(var1_simplified)$var1_a.nn.deg_w=="NaN"]<-0
#PLotting
plot(var1_simplified,
layout=co,
edge.color=V(var1_simplified)$color[edge.start],
edge.arrow.size=sqrt((V(var1_simplified)$var1_a.nn.deg_w)^2+1)/1000,
edge.width=E(var1_simplified)$weight/100,
edge.curved = TRUE,
vertex.color=V(var1_simplified)$color,
vertex.size=(sqrt((V(var1_simplified)$var1_a.nn.deg_w)^2))/5,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(var1_simplified,"LABEL_COR"),
vertex.label.cex=(sqrt((V(var1_simplified)$var1_a.nn.deg_w)^2)+1)/500,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(var1_simplified)$LABEL_COR
b<-V(var1_simplified)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=4,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Average Weighted Neighbor Degree Sized - 3_REFERENCIA DE ENVIO (var1)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median Average Weighted Neighbor Degree: %.2f",
median((var1_a.nn.deg_w+1))
))
#Circle Degree ***Too intense computation***
#A_var1 <- get.adjacency(var1, sparse=FALSE)
#detach("package:igraph", unload=TRUE)
#library(network)
#g <- network::as.network.matrix(A_var1)
#library(sna)
#gplot.target(g, degree(g), main="Circle Degree")
#library(igraph)
save.image("~/SNArRDJF/Robject/var1_data.RData")